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Browsing by Author "Dembinski, Gina M."
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Item Advancements in forensic DNA-based identification(2017) Dembinski, Gina M.; Picard, Christine; Christie, Mark; Walsh, Susan; Randall, Stephen; Goodpaster, JohnModern DNA profiling techniques have increased in sensitivity allowing for higher success in producing a DNA profile from limited evidence sources. However, this can lead to the amplification of more DNA profiles that do not get a hit on a suspect or DNA database and more mixture profiles. The work here aims to address or improve these consequences of current DNA profiling techniques. Based on allele-specific PCR and quantitative color measurements, a 24-SNP forensic phenotypic profile (FPP) assay was designed to simultaneously predict eye color, hair color, skin color, and ancestry, with the potential for age marker incorporation. Bayesian Networks (BNs) were built for model predictions based on a U.S sample population of 200 individuals. For discrete pigmentation traits using an ancestry influenced pigmentation prediction model, AUC values were greater than 0.65 for the eye, hair, and skin color categories considered. For ancestry using an all SNPs prediction model, AUC values were greater than 0.88 for the 5 continental ancestry categories considered. Quantitative pigmentation models were also built with prediction output as RGB values; the average amount of error was approximately 7% for eye color, 12% for hair color, and 8% for skin color. A novel sequencing method, methyl-RADseq, was developed to aid in the discovery of candidate age-informative CpG sites to incorporate into the FPP assay. There were 491 candidate CpG sites found that either increased or decreased with age in three forensically relevant xii fluids with greater than 70% correlation: blood, semen, and saliva. The effects of exogenous microbial DNA on human DNA profiles were analyzed by spiking human DNA with differing amounts of microbial DNA using the Promega PowerPlex® 16 HS kit. Although there were no significant effects to human DNA quantitation, two microbial species, B. subtilis and M. smegmatis, amplified an allelic artifact that mimics a true allele (‘5’) at the TPOX locus in all samples tested, interfering with the interpretation of the human profile. Lastly, the number of contributors of theoretically generated 2-, 3-, 4-, 5-, and 6-person mixtures were evaluated via allele counting with the Promega PowerPlex® Fusion 6C system, an amplification kit with the newly expanded core STR loci. Maximum allele count in the number of contributors for 2- and 3-person mixtures was correct in 99.99% of mixtures. It was less accurate in the 4-, 5-, and 6-person mixtures at approximately 90%, 57%, and 8%, respectively. This work provides guidance in addressing some of the limitations of current DNA technologies.Item Effects of Microbial DNA on Human Forensic DNA Profiles(Office of the Vice Chancellor for Research, 2016-04-08) Dembinski, Gina M.Most biological evidence obtained from crimes scenes is found in non-sterile environments, and therefore there is an opportunity for the forensic samples to become contaminated with environmental DNA. In particular, the effects of microbial species that may contaminate forensic samples have not been extensively studied. This type of information could be especially important in cases where decomposition has occurred and microbes are in abundance. We investigated the environmental effects on DNA samples via contamination from microbial sources by intentionally spiking human DNA samples with known concentrations of DNA from 17 common microbe species associated with human decomposition. Quantitation and amplification was performed using the Quantifiler® Human DNA quantitation kit (Thermo Fisher Scientific, Inc.) and PowerPlex® 16 HS system (Promega Corp.), respectively. Single species of microbial DNA were added in varying quantities (1, 50, 100 ng) to a standard 1 ng human DNA sample. Results indicate that there was little to no effect on quantitation of the human DNA samples. However during genotyping, two species, Bacillus subtilis and Mycobacterium smegmatis, produced an artifact peak at the TPOX locus. The same artifact was still amplified when the DNA of the two species was tested in the absence of human DNA. This type of data will impact the forensic community as it demonstrates that microbial contamination may affect the development and interpretation of a human DNA profile. As most forensic genotyping kits are developed to be human specific, these results indicate that caution should still be used in interpreting the human DNA profile, especially in cases of decomposition in which there would be higher levels of microbial contamination.Item Estimation of the number of contributors of theoretical mixture profiles based on allele counting: Does increasing the number of loci increase success rate of estimates?(Elsevier, 2018-03) Dembinski, Gina M.; Sobieralski, Carl; Picard, Christine J.; Biology, School of ScienceDNA mixtures are more frequently encountered in casework due to increased kit sensitivity, protocols with increased cycle number, and requests for low copy number DNA samples to be tested. Generally, the first step in mixture interpretation is determining the number of contributors, with the most common approach of maximum allele count. Although there are previous studies regarding the accuracy of this approach, none have evaluated the accuracy with the newly expanded U.S. core STR loci. In this work, 4,976,355 theoretical mixture combinations were generated with the PowerPlex® Fusion 6C system which includes 23 autosomal STR loci and three Y-STR loci. The number of contributors could be correctly assumed for 100% two-person and 99.99% three-person mixtures, whereas, four-, five-, and six-person mixtures were correctly assumed in 89.7%, 57.3%, and 7.8% of mixtures, respectively. Y-STR analysis showed the 3 Y-STR markers are only accurate for two-person male mixtures (96.7%). This work demonstrates that maximum allele count using the expanded U.S. core loci is not much improved from previous smaller panels, reiterating that this method is not as accurate beyond three contributors.Item Evaluation of the IrisPlex DNA-based eye color prediction tool in the United States(2014-07-31) Dembinski, Gina M.; Picard, Christine; Randall, Stephen Karl, 1953-; Goodpaster, John V. (John Vincent)DNA phenotyping is a rapidly developing area of research in forensic biology. Externally visible characteristics (EVCs) can be determined based on genotype data, specifically from single nucleotide polymorphisms (SNPs). These SNPs are chosen based on their association with genes related to the phenotypic expression of interest, with known examples in eye, hair, and skin color traits. DNA phenotyping has forensic importance when unknown biological samples at a crime scene do not result in a criminal database hit; a phenotype profile of the sample can therefore be used to develop investigational leads. IrisPlex, an eye color prediction assay, has previously shown high prediction rates for blue and brown eye color in a European population. The objective of this work was to evaluate its utility in a North American population. We evaluated the six SNPs included in the IrisPlex assay in an admixed population sample collected from a U.S.A. college campus. We used a quantitative method of eye color classification based on (RGB) color components of digital photographs of the eye taken from each study volunteer and placed in one of three eye color categories: brown, intermediate, and blue. Objective color classification was shown to correlate with basic human visual determination making it a feasible option for use in future prediction assay development. In the original IrisPlex study with the Dutch samples, they correct prediction rates achieved were 91.6% for blue eye color and 87.5% for brown eye color. No intermediate eyes were tested. Using these samples and various models, the maximum prediction accuracies of the IrisPlex system achieved was 93% and 33% correct brown and blue eye color predictions, respectively, and 11% for intermediate eye colors. The differences in prediction accuracies is attributed to the genetic differences in allele frequencies within the sample populations tested. Future developments should include incorporation of additional informative SNPs, specifically related to the intermediate eye color, and we recommend the use of a Bayesian approach as a prediction model as likelihood ratios can be determined for reporting purposes.